import argparse import logging import os import numpy as np import torch import torch.nn.functional as F from PIL import Image from torchvision import transforms from utils.data_loading import BasicDataset from unet import UNet from utils.utils import plot_img_and_mask def predict_img(net, full_img, device, scale_factor=1, out_threshold=0.5): net.eval() img = torch.from_numpy(BasicDataset.preprocess(full_img, scale_factor, is_mask=False)) img = img.unsqueeze(0) img = img.to(device=device, dtype=torch.float32) with torch.no_grad(): output = net(img) if net.n_classes > 1: probs = F.softmax(output, dim=1)[0] else: probs = torch.sigmoid(output)[0] tf = transforms.Compose([ transforms.ToPILImage(), transforms.Resize((full_img.size[1], full_img.size[0])), transforms.ToTensor() ]) full_mask = tf(probs.cpu()).squeeze() if net.n_classes == 1: return (full_mask > out_threshold).numpy() else: return F.one_hot(full_mask.argmax(dim=0), net.n_classes).permute(2, 0, 1).numpy() def get_args(): parser = argparse.ArgumentParser(description='Predict masks from input images') parser.add_argument('--model', '-m', default='MODEL.pth', metavar='FILE', help='Specify the file in which the model is stored') parser.add_argument('--input', '-i', metavar='INPUT', nargs='+', help='Filenames of input images', required=True) parser.add_argument('--output', '-o', metavar='INPUT', nargs='+', help='Filenames of output images') parser.add_argument('--viz', '-v', action='store_true', help='Visualize the images as they are processed') parser.add_argument('--no-save', '-n', action='store_true', help='Do not save the output masks') parser.add_argument('--mask-threshold', '-t', type=float, default=0.5, help='Minimum probability value to consider a mask pixel white') parser.add_argument('--scale', '-s', type=float, default=0.5, help='Scale factor for the input images') return parser.parse_args() def get_output_filenames(args): def _generate_name(fn): split = os.path.splitext(fn) return f'{split[0]}_OUT{split[1]}' return args.output or list(map(_generate_name, args.input)) def mask_to_image(mask: np.ndarray): if mask.ndim == 2: return Image.fromarray((mask * 255).astype(np.uint8)) elif mask.ndim == 3: return Image.fromarray((np.argmax(mask, axis=0) * 255 / mask.shape[0]).astype(np.uint8)) if __name__ == '__main__': args = get_args() in_files = args.input out_files = get_output_filenames(args) net = UNet(n_channels=3, n_classes=2) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') logging.info(f'Loading model {args.model}') logging.info(f'Using device {device}') net.to(device=device) net.load_state_dict(torch.load(args.model, map_location=device)) logging.info('Model loaded!') for i, filename in enumerate(in_files): logging.info(f'\nPredicting image {filename} ...') img = Image.open(filename) mask = predict_img(net=net, full_img=img, scale_factor=args.scale, out_threshold=args.mask_threshold, device=device) if not args.no_save: out_filename = out_files[i] result = mask_to_image(mask) result.save(out_filename) logging.info(f'Mask saved to {out_filename}') if args.viz: logging.info(f'Visualizing results for image {filename}, close to continue...') plot_img_and_mask(img, mask)